Previous attempts of using artificial intelligence to determine the location and positioning of an object included aligning 3D models parameterized in terms of low-level primitives such as blocks, generalized cylinders, and deformable superquadratics to images. One such attempt included aligning 3D models represented as 3D meshes of vertices and faces to images. This attempt was limited to instances where the target object was “clean” such that the target object was composed of low-level primitives, the images thereof had strong gradients, and/or the underlying 3D shapes precisely matched a known 3D model. Solutions to this issue have resulted in approaches that have focused on developing representations, such as histograms of gradients, to improve the similarity between images by providing invariance to low-level features. However, the invariance to low-level features has precluded precise alignment.
Accordingly, a need exists for a fully automated approach to perform precise alignment of 3D models to objects in single images of the objects.